In large-scale partial equilibrium models, the numerical description of forest age class dynamics and the representation of forest management are problematic  . Natural conditions (site productivity) and forest management (species composition, rotation length, thinning intensity) determine the age structure of the forest and current forest productivity. With information on the age structure of forest, its growth function, and forest management options (e.g., several rotation lengths), we can estimate forest productivity development.
We develop a forest management model based on applied linear programming. We use predefined forest management options estimated in G4M  to choose the optimal volume of harvesting for each simulation period. Forest age structure and biomass is updated according to the chosen forest management option and passed to the next optimization period. As these forest management options are modeled in the dynamic global forest model G4M, G4M input data and modeling results, in particular, are used for parametrization of the forest management module.
Our forest management model included 16 age cohorts. Each age cohort contains data about forest area, biomass growing stock per ha, and sawn logs potential per ha. Wood demand and wood prices are given endogenously. The aim of the model is to maximize forest revenues in every simulation period, the latter being 10 years. Rotation time for forest is also endogenous, but the final decision regarding in which age cohort wood will be harvested is taken by the model itself. All calculations are done at a country level. For some countries real age structure distribution is used if it is available. If there is no age distribution within a country available, we assume that we are dealing with normal forest.
Availability of age structure distribution allows a more accurate estimation to be made of where forest was harvested or where it can be harvested. Moreover, using rotation time different forest management practices and regimes can be experimented with. A very important issue is to find proper data so that the forest growth can be correctly estimated so that the model will produce realistic results.
 Hertel T, Rose S, Tol R (2009). Economic analysis of land use in global climate change policy. Abingdon, UK. Routledge. 348 pp.
 Havlík P et al (2011). Global land-use implications of first and second generation biofuel targets. Energy Policy, 39(10): 5690-5702
 Kindermann G, Schoerghuber S, Linkosalo T, Sanchez A, Rammer W, Seidl R, Lexer MJ (2013). Potential stocks and increments of woody biomass in the European Union under different management and climate scenarios. Carbon Balance and Management 8(1),2.
Nicklas Forsell and Mykola Gusti, Ecosystems Services and Management, IIASA
Olga Turkovska of the Lviv Polytechnic National University, Ukraine, is a citizen of Ukraine. She raised private funds and worked in the Ecosystems Services and Management (ESM) Program during the YSSP.
Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.
Last edited: 29 September 2015
International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313